*Assessment of Robustness of cross-sectional models, Waves 3 and 4 of AMPS

*note: before conducting analysis in Stata, need to replace all "NAs" in csv file with "."

cd "YOUR DIRECTORY"

*Wave 3 analysis

*import data

import delimited "wave3_whites.csv", clear

*estimate model (without survey weights) to get point estimate and SE

regress grt_beliefs fox_news male  age  education  income  ideology  party_id  fire_scale 

mrobust regress grt_beliefs fox_news male  age  education  income  ideology  party_id  fire_scale, pref(.1095444, .0196554)

*fox news is positive and statistically significant in 100% of models, and preferred estimate is in 22% of distribution

*repeat analysis for continuous measure
regress grt_beliefs fox_total male  age  education  income  ideology  party_id  fire_scale 

mrobust regress grt_beliefs fox_total male  age  education  income  ideology  party_id  fire_scale, pref(.2083182, .0414913)

*fox total is positive and statistically significant in 100% of models, and preferred estimate is in 30% of distribution

*Wave 4 analysis

import delimited "wave4_whites.csv", clear

*estimate model (without survey weights) to get point estimate and SE

regress grt_beliefs fox_news male  age  education  income  ideology  party_id  fire_scale 

mrobust regress grt_beliefs fox_news male  age  education  income  ideology  party_id  fire_scale, pref( .0754587,  .0192107)

*fox news is positive and statistically significant in 100% of models, and preferred estimate is in 11% of distribution

*repeat for continuous measure

regress grt_beliefs fox_total male  age  education  income  ideology  party_id  fire_scale 

mrobust regress grt_beliefs fox_total male  age  education  income  ideology  party_id  fire_scale, pref(.1435295, .0389652)

*fox total is positive and statistically signifiacnt in 100% of models, and preferred estimate is in 9th percentile of modeling distribution
